Decentralized and Dynamic Bandwidth Allocation ... - Semantic Scholar

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Decentralized and Dynamic Bandwidth Allocation in Networked Control Systems Ahmad T. Al-Hammouri, Michael S. Branicky, Vincenzo Liberatore Case Western Reserve University Stephen M. Phillips Arizona State University April 25, 2006 Support by: NSF CCR-0329910, Department of Commerce TOP 39-6004003, NASA NNC04AA12A, and an OhioICE Training grant

Paper Overview „ Control over Networks „ NCSs, DCSs, SANETs, …

„ Control of Networks „ Efficient BW allocation „ Congestion control

„ Fairness

„ We propose a “Cof N” scheme to better serve “Cover N” Ahmad Al-Hammouri

Decentralized and Dynamic Bandwidth Allocation in Networked Control Systems (WPDRTS’06)

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Control over Networks

Ahmad Al-Hammouri

Decentralized and Dynamic Bandwidth Allocation in Networked Control Systems (WPDRTS’06)

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Control over Networks

Ahmad Al-Hammouri

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Control over Networks

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Decentralized and Dynamic Bandwidth Allocation in Networked Control Systems (WPDRTS’06)

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Control over Networks

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Decentralized and Dynamic Bandwidth Allocation in Networked Control Systems (WPDRTS’06)

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Control over Networks

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Control over Networks

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Decentralized and Dynamic Bandwidth Allocation in Networked Control Systems (WPDRTS’06)

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Control over Networks

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Decentralized and Dynamic Bandwidth Allocation in Networked Control Systems (WPDRTS’06)

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Control over Networks

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Control over Networks

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Control over Networks „ Remote interaction (monitoring & control) with the physical world „ Applications: „ „ „ „

Industrial automation & process control Space exploration, e.g., telerobotics Smart homes Medical sensing & surgical simulations „ Automatic asset mgmt. (RFID) Ahmad Al-Hammouri

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Control of Networks (Scope of the paper) „ A bandwidth allocation scheme „ Objectives: „ „ „ „

Stability of control systems Efficiency & fairness Fully distributed, asynchronous, & scalable Dynamic & self reconfigurable

„ Formulating the scheme in CT „ NCSs regulate h based on congestion fed back from the network Ahmad Al-Hammouri

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Sampling Rate & Network Congestion h=1/r

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Sampling Rate & Network Congestion

l1

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l2

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Problem Formulation „ Define a utility fn U(r) that is „ Monotonically increasing „ Strictly concave „ Defined for r ≥ rmin

„ Optimization formulation max ∑ i Ui (ri ) s.t.



i∈S ( l )

ri ≤ Cl , l = 1,..., L

and ri ≥ r min, i Ahmad Al-Hammouri

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Distributed Implementation „ Two independent algorithms „ End-systems (plants) algorithm „ Router algorithm (later on) NCS Plant

Router

NCS Controller

p

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Distributed Implementation „ Two independent algorithms „ End-systems (plants) algorithm „ Router algorithm (later on) NCS Plant

Router

NCS Controller

p

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Distributed Implementation „ Two independent algorithms „ End-systems (plants) algorithm „ Router algorithm (later on) NCS Plant

Router

NCS Controller

p

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Distributed Implementation „ Two independent algorithms „ End-systems (plants) algorithm „ Router algorithm (later on) NCS Plant

Router

NCS Controller

p

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Distributed Implementation „ Two independent algorithms „ End-systems (plants) algorithm „ Router algorithm (later on) NCS Plant

Router

NCS Controller p

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Distributed Implementation „ Two independent algorithms „ End-systems (plants) algorithm „ Router algorithm (later on) NCS Plant

Router

NCS Controller p

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Distributed Implementation „ Two independent algorithms „ End-systems (plants) algorithm „ Router algorithm (later on) NCS Plant

Router

NCS Controller p

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Distributed Implementation „ Two independent algorithms „ End-systems (plants) algorithm „ Router algorithm (later on) NCS Plant

NCS Controller

Router p

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Distributed Implementation „ Two independent algorithms „ End-systems (plants) algorithm „ Router algorithm (later on) NCS Plant

Router

NCS Controller

p

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Distributed Implementation „ Two independent algorithms „ End-systems (plants) algorithm „ Router algorithm (later on) NCS Plant

Router

NCS Controller

p

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Distributed Implementation „ Two independent algorithms „ End-systems (plants) algorithm „ Router algorithm (later on) NCS Plant

Router

NCS Controller

p

r ( pt ) = 1 h = ⎡⎣U ' ( pt ) ⎤⎦ −1

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r max r min

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NCS-AQM Control Loop

NCS Plant r ( p ) = U '−1 ( p)

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NCS-AQM Control Loop

NCS Plant

Queue

r ( p ) = U '−1 ( p)

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NCS-AQM Control Loop

NCS Plant r ( p ) = U '−1 ( p)

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Queue tf

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NCS-AQM Control Loop

NCS Plant −1

r ( p) = U ' ( p)

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Queue tf

q`=Σr(t) - C

q(t)

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NCS-AQM Control Loop

NCS Plant r ( p ) = U '−1 ( p)

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Queue tf

q`=Σr(t) - C

q(t)

Decentralized and Dynamic Bandwidth Allocation in Networked Control Systems (WPDRTS’06)

p(t) f(q(t))

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NCS-AQM Control Loop

NCS Plant r ( p ) = U '−1 ( p)

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Queue tf

q`=Σr(t) - C

q(t)

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p(t) f(q(t))

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NCS-AQM Control Loop

NCS Plant r ( p ) = U '−1 ( p)

Queue tf

q`=Σr(t) - C

q(t)

p(t) f(q(t))

tb

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NCS-AQM Control Loop

NCS Plant

Model Plant q`=Σr(t) - C r ( p) = U ' ( p) t B − sd P(s) = s e −1

Queue q(t)

f

p(t) f(q(t))

tb

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NCS-AQM Control Loop

NCS Plant

Model Plant q`=Σr(t) - C r ( p) = U ' ( p) t B − sd P(s) = s e −1

f

Queue q(t)

Queue p(t) f(q(t)) Controller G(s)

tb

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Queue Controller G(s) „ Proportional (P) Controller „ GP(s) = kp

„ Proportional-Integral (PI) Controller „ GPI(s) = kp + ki/s q0 +

Ahmad Al-Hammouri

e _

G(s)

u

q(s) P(s)

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Determination of kp and ki „ Stability region in the ki–kp plane „ Stabilizes the NCS-AQM closed-loop system for delays less or equal d

„ Analysis of quasi-polynomials, f(s,es)

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u (tj ) = − K ( R − x(tj ))

Simulations & Results 50 NCS Plants: dx = ax(t ) + bu (t ) dt a − bK a / r • U (r ) = e a



a • r min = ln bK + a bK − a [Branicky et al. 2002] [Zhang et al. 2001]

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Simulations & Results (cont.)

PI ¤ P ¤

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Simulations & Results (cont.)

PI ¤ P ¤

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Simulations & Results (cont.)

PI ¤ P ¤

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Simulations & Results (cont.)

PI ¤ P ¤

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Simulations & Results (cont.)

PI ¤ P ¤

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Thank You „ Questions „ Comments „ Probing further: http://start.case.edu/~vxl11/NetBots

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